1. Field
The present disclosure relates generally to data management and, in particular, to allowing a classification system to more quickly make a classification determination from an input data set.
2. Background
Classification systems receive data, analyze the data, and then assign the data to a known set, or classifier, where one or more elements of the data correspond to one or more elements of the classifier. For example, in a human motion detection classification system, sensors may measure actions of a human. These sensors may input their data to a classification system, which then analyze the data to determine which action the data most resembles. Examples of such a classification may be to classify whether the human sits, stands, walks, holds a phone, bends over, or takes some other action. In another example, a classification system could analyze the input from sensors on an aircraft and then classify some aspect of the aircraft's operation, such as whether the aircraft executes a turn or whether flaps have been deployed.
However, in some cases, the amount of input data may overwhelm a classification system. By “overwhelm” what is meant is that enough input data is presented, or certain types of data are present, that the system cannot produce—or has a substantial chance of failing to produce—a classification within a desired period of time to a desired degree of accuracy. The term “overwhelming amount of data” also includes a situation where a classification system or computer system could handle a volume of data, but a network transmitting the data could not. Thus, methods and devices are desirable that provide a method for obtaining relevant information to classify new observations within data sets, without sacrificing speed and accuracy.